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@ARTICLE{Walter:287260,
      author       = {A. Walter$^*$ and P. Hoegen-Saßmannshausen$^*$ and G.
                      Stanic$^*$ and J. P. Rodrigues$^*$ and S. Adeberg and O.
                      Jäkel$^*$ and M. Frank and K. Giske$^*$},
      title        = {{S}egmentation of 71 {A}natomical {S}tructures {N}ecessary
                      for the {E}valuation of {G}uideline-{C}onforming {C}linical
                      {T}arget {V}olumes in {H}ead and {N}eck {C}ancers.},
      journal      = {Cancers},
      volume       = {16},
      number       = {2},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2024-00192},
      pages        = {415},
      year         = {2024},
      note         = {#EA:E040#LA:E040#},
      abstract     = {The delineation of the clinical target volumes (CTVs) for
                      radiation therapy is time-consuming, requires intensive
                      training and shows high inter-observer variability.
                      Supervised deep-learning methods depend heavily on
                      consistent training data; thus, State-of-the-Art research
                      focuses on making CTV labels more homogeneous and strictly
                      bounding them to current standards. International consensus
                      expert guidelines standardize CTV delineation by
                      conditioning the extension of the clinical target volume on
                      the surrounding anatomical structures. Training strategies
                      that directly follow the construction rules given in the
                      expert guidelines or the possibility of quantifying the
                      conformance of manually drawn contours to the guidelines are
                      still missing. Seventy-one anatomical structures that are
                      relevant to CTV delineation in head- and neck-cancer
                      patients, according to the expert guidelines, were segmented
                      on 104 computed tomography scans, to assess the possibility
                      of automating their segmentation by State-of-the-Art deep
                      learning methods. All 71 anatomical structures were
                      subdivided into three subsets of non-overlapping structures,
                      and a 3D nnU-Net model with five-fold cross-validation was
                      trained for each subset, to automatically segment the
                      structures on planning computed tomography scans. We report
                      the DICE, Hausdorff distance and surface DICE for 71 + 5
                      anatomical structures, for most of which no previous
                      segmentation accuracies have been reported. For those
                      structures for which prediction values have been reported,
                      our segmentation accuracy matched or exceeded the reported
                      values. The predictions from our models were always better
                      than those predicted by the TotalSegmentator. The sDICE with
                      2 mm margin was larger than $80\%$ for almost all the
                      structures. Individual structures with decreased
                      segmentation accuracy are analyzed and discussed with
                      respect to their impact on the CTV delineation following the
                      expert guidelines. No deviation is expected to affect the
                      rule-based automation of the CTV delineation.},
      keywords     = {anatomical structures (Other) / automatic segmentation
                      (Other) / clinical target volume delineation (Other) /
                      expert guidelines (Other) / head and neck cancer (Other) /
                      lymph-node-level segmentation (Other) / multi-label
                      segmentation (Other)},
      cin          = {E040 / E050},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)E050-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38254904},
      doi          = {10.3390/cancers16020415},
      url          = {https://inrepo02.dkfz.de/record/287260},
}